The proliferation of video content delivery during recent years has necessitated the development of objective video quality assessment methods. It has become evident that network parameters which define the Quality of Service (QoS) are not sufficient to estimate the service quality perceived by the user, typically referred to as Quality of Experience (QoE).
Quality estimation methods commonly support a distinguished estimation of the quality related to the coding (compression, Qcod) of the video signal and the quality due to packet loss during transmission (Qtrans). It is an inherent property of IP-networks that (mostly due to temporal overload at some point in the network) IP packets may be lost. Some of these losses may be almost invisible to the customer while others may cause severe degradation of the video quality. Even if countermeasures against these losses are part of an IPTV distribution system, these countermeasures can never guarantee an absolute remedy. For instance, a retransmission request may take too long, or the retransmitted packet itself might get lost. Therefore there is always a nonzero probability, that fragmentary bit streams are transmitted to the end user device. These in turn can cause visible or audible degradations in the reconstructed video. Measurement categories may therefore also include values to express the probability for losses. Such values may include the expression of a “packet loss rate” and the “burstiness of loss events”.
For this purpose, parametric video quality models for IPTV applications have been developed which account for the distortion both due to compression and erroneous transmission (see, e.g., K. Yamagishi and T. Hayashi, “Parametric Packet-Layer Model for Monitoring Video Quality of IPTV Services,” in Proc. of IEEE Int. Conf. on Communications, 2008 [1], or M. N. Garcia and A. Raake, “Parametric packet-layer video quality model for IPTV,” Int. Conf. on Information Science, Signal Processing and their Applications (ISSPA), Kuala-Lumpur, May 2010 [2]).
However, purely header-based models cannot precisely cover the impact of network impairments on visual quality in terms of the spatio-temporal characteristics of the video sequence and the statistical properties of the packet losses. Thus, objective video quality assessment models should analyze the relationship between packet losses and visual degradation and factor in the fact that packet losses do not produce an equal amount of perceived degradation.
The problem of predicting packet loss visibility has been addressed in the literature only in terms of classification of packet losses in a binary manner: visible or invisible. In S. Kanumuri, P. C. Cosman, A. R. Reibman, V. A. Vaishampayan, “Modeling packet-loss visibility in MPEG-2 video,” IEEE Trans. On Multimedia, vol. 8, no. 2, April 2004, pp. 341-355, a set of features was extracted from the MPEG-2 bitstream and two modeling approaches, a Generalized Linear Model (GLM) was used to estimate the relative number of viewers who detected an error, and a tree classifier to determine whether a packet loss results in a visible degradation. The former classification was extended for H.264/AVC video in S. Kanumuri, S. B. Subramanian, P. C. Cosman, A. R. Reibman, “Predicting H.264 packet loss visibility using a generalized linear model,” in Proc. of IEEE Int. Conf. on Image Processing (ICIP), Atlanta, Ga., October 2006, in which the effect of dual packet losses was examined, and in T. L. Lin, S. Kanumuri, Y. Zhi, D. Poole, P. C. Cosman, and A. R Reibman, “A versatile model for packet loss visibility and its application to packet prioritization,” IEEE Trans. on Image Processing, vol. 19, no. 3, pp. 722-735, March 2010, where the proposed framework was used for packet prioritization at the intermediate routers of a network.
Additionally, a no-reference bit stream-based decision tree classifier for CIF sequences was developed by N. Staelens et al., “Viqid: A no-reference bit stream-based visual quality impairment detector,” in IEEE Workshop on Quality of Multimedia Experience, Trondheim, Norway, June 2010. Here, the effect of the packet loss pattern and length on the subjective quality still remains an open question. In Y. J. Liang, J. G. Apostolopoulos, and B. Girod, “Analysis of packet loss for compressed video: effect of burst losses and correlation between error frames,” IEEE Trans. on Circuits and Systems for Video Technology, vol. 18, no. 7, pp. 861-874, July 2008, the effect of burst losses on the reconstructed video quality was analyzed and it was shown that a specific loss pattern produces a larger degradation than an equal number of isolated losses. Also, the correlation between error frames was considered in the modeling of the induced distortion. However, the algorithm was only tested on QCIF sequences and, thus, with a packetization scheme in which an individual frame is contained in one packet. Moreover, the impact on the subjective ratings was not tested.